1143: AI Literacy and the Future of Public Health Work

JOHN SHEEHAN: 

This is Public Health Review Morning Edition for Wednesday, June 10, 2026. I'm John Sheehan with news from the Association of State and Territorial Health Officials.

Today, AI literacy and the future of public health work. Ari Whiteman, a senior advisor for public health data and informatics workforce at ASTHO, talks about the growing importance of AI literacy across the public health field. He'll explain why AI literacy goes beyond simply knowing how to use new tools and why public health professionals need practical, real-world training to safely integrate AI into their work.

ARI WHITEMAN: 

AI literacy refers to essentially the ability to understand, evaluate, use, and ethically navigate AI technologies. It refers to understanding all the different various use cases that are out there, in terms of both their benefits and their costs, understanding where bias might be incurred, where value can be gained. So, it's really comprehensive of the entire experience around AI. It's not just how it's useful, but where potential risks can occur as well.

SHEEHAN: 

For sure. And that brings up a good use case for why it's important for new hires in public health to specifically know about the uses and dangers of AI, and what can universities do to better prepare students to get into the real world?

WHITEMAN: 

So, currently, really, only a subset of graduate programs, public health graduate programs, have AI literacy baked into their curriculum. I think that's something that needs to change over time. There is a tremendous demand in the workforce, not just the governmental workforce, but the consultants and nonprofits that are supporting governments as well, for AI literacy, for new hires to understand where AI can be applied safely, ethically, and how to critically analyze and understand the outputs of AI. Including that into the curriculum for graduate education, I think, is of vital importance moving forward for the public health workforce.

SHEEHAN: 

And so, what are the uses of AI in a public health context, and what should new hires be more aware of?

WHITEMAN: 

So, in terms of the use cases, it really spans the gamut of public health, everything from, you know, more specific public health type processes, like surveillance, to administrative use cases, communications, and everything in between. Grant administration, it really is spanning the gamut of what's possible within a public health agency, in terms of the skills that are needed to work on those. It really depends on the role that students or anybody who's entering the workforce is interested in going into. I would say that, for example, if you're an epidemiologist, if you're studying epidemiology, and you're looking to go into working on surveillance systems, I would say it's particularly important that, from an AI perspective, you understand how AI can be applied to syndromic surveillance, to EHR interoperability standards, and, as well, in terms of understanding the risks, knowing how that data is processed, the privacy standards that are in place and required for electronic health records to be maintained and run through AI models, understanding how that works, and really being fluent in those methods, both the risks and benefits, is of particular importance. But I mean, maybe you're also interested in going into health communications. They have a very different set of needs. So, let's say you know you want to be working on social media or writing press releases, you need to make sure that if you're going to be using AI for those processes, you can understand how to properly validate the information that it's developing for you. Maybe you work on or gain familiarity with what's called a RAG model, which essentially creates content only using the sources that you provide it. It doesn't use the entire rest of the internet, so, you know, being familiar with something like a RAG architecture might be important if you're going into working on health communications AI. So, it really is diverse in terms of the skills that are necessary, depending on the role.

SHEEHAN: 

There's such an opportunity for these systems that can process so much data and turn things around quickly, which is especially useful in a sector like public health, but it's also super dangerous because there are such privacy concerns, and the stakes are so much higher because everything has to be verified by a human.

WHITEMAN: 

Right. You don't want to sacrifice speed for quality. You want to ensure that even if you are cutting out time for your workflow and your processes, that you are evaluating and validating everything that's spitting out at the end, and that's really important for students to know, too. They don't know, we don't want students to come into roles assuming that they can just 'plug and play,' you know, use AI like a vending machine. We really think it's important that they come out with critical thinking skills and critical analysis skills to be able to look at an output from AI, and understand, for example, where it's wrong, or understand where it's inappropriate for a given context that you know, that you're using it for. You know, AI is not a product; it's a method. And so, being able to appropriately engage with this method safely, and ensuring that you know data is secure, ensuring that you don't put out, you know, faulty information out to the public, that's of incredible concern. And so, having training programs, graduate training programs, ensures that these types of validation frameworks are baked into their AI literacy programs, if they even exist, that that would be particularly valuable.

SHEEHAN: 

Yeah. What are you seeing as the biggest gaps between what graduate students, what new hires can do with AI, and what public health agencies actually need?

WHITEMAN: 

So, you know, it's interesting when we look at the data on AI implementation across the country. We see that quite a high number, upwards of 70% of states and territories that have responded to our surveys, have AI policies in place, and most of these policies concern governance, they concern data security and privacy. What they don't necessarily concern is workforce skills and guidance. And that's really one of the major hurdles or challenges towards further implementation of AI; is that workforces don't necessarily understand where these tools should be applied. They don't necessarily have the guidance to know, you know, this is a good use case, this is not a good use case. And so, that's a particular gap in the current workforce that exists. And so, I think that's where we can look to training programs to fill that gap. So, if training programs are able to better strengthen their education, their curriculum around, you know, technical basics, ethics, hands-on learning, policy, governance, you know, operations, all of that revolving around AI, these students can enter the workforce and be able to tell their staff and their co-workers, this is a good use case. This is not a good use case, you know. This particular model is better than this particular model, and have that fluency, that literacy in the methodology to be able to close that gap and allow AI to be implemented further than it's been so far.

SHEEHAN: 

Yeah, and with that in mind, how would you change or redesign current training methods?

WHITEMAN: 

So, it's, you know, it's been a while since I was in school, but I, you know, from what I, you know, can understand as someone who works on behalf of the public health system, is I really think training needs to be more practical. And this isn't just training in terms of graduate training, but training, you know, of existing staff. AI literacy training, it really needs to be based on use cases. It needs to be tied to real-world job tasks and planned really in collaboration with people who are either going to be hiring for those roles or who are already doing it themselves. So, coming up with, you know, esoteric traditional examples that may have been in place for public health 10 years ago is not doing anybody any favors these days. We really need to know how these technologies work in real-world scenarios today, or you know, tomorrow. So, you know, building and building in those more real-world examples, and having students come up with, for example, prompts, or, you know, to satisfy, you know, certain scenarios, or maybe have them evaluate prompts, or evaluate results for accuracy, rather. You know, those types of exercises are, I think, really going to allow students to put themselves in the shoes of, ideally, their employers, and, you know, be able to interview better, and hopefully land the roles that they want to.

SHEEHAN: 

Yeah. And for students, and actually I would go so far as to say students, new hires, long-term employees, veterans of the field, what can we all do to start increasing our own AI literacy?

WHITEMAN: 

Yeah, so I'm glad you pointed out that difference there, because AI literacy is not just, you know, a need for students. I mean, everybody from the executive suites down to the, you know, analyst level to current graduates to undergraduates. I think should be gaining literacy in these methods, because they're not going away, they're becoming easier to use, they're becoming more effective, and they're in their use cases. And so, they're only going to be further proliferating across the industry, so gaining literacy in again everything from its use cases to its risks, and benefits, and values. And communicating those different attributes is of particular importance in terms of what I've tried to do myself. I've tried to read up on how AI is impacting my particular area of public health, so maybe that's how AI is impacting workforce training, or how AI is impacting data quality improvements, or disease surveillance, and the ways that it's having impacts in those different, you know, areas are quite different, so it's, you know, it's not just an industry as a whole, how it's impacting things, but it's really sort of task by task. And so, reading up about that is particularly valuable. I also think attending conferences and being involved in several different, you know, there's different communities of practice and peer networks that are out there, where people are sharing use cases amongst their peers, you know, showing what works, what didn't work, what lessons they've learned. Those types of networks are particularly valuable to learning what's out there, what's available, and what the potential, you know, limits, and risks, and values are of these different use cases. I think specifically for students, if you don't have AI incorporated into your curriculum right now, it's really, you know, the onus is on you to gain those skills and experience on your own, incorporating AI as a, maybe as a method in your practicum or in your thesis research. It's something that, you know, no one's going to necessarily tell you to do, but it's something that's probably going to benefit you in the long run, in terms of the job hunts, you know, taking that initiative and understanding that this is the direction that the field is going, and incorporating those methods in your research, in your training, is something that oftentimes you know, without any guidance, you have to do yourself. And so, understanding that, that's the sort of momentum that's taking place right now, and you know, seeking out those opportunities. I think that's that would be particularly wise.

SHEEHAN: 

Ari Whiteman is a senior advisor for public health data and informatics workforce at ASTHO.

Join ASTHO and the Public Health Foundation on Thursday, June 18, for an insightful webinar exploring the power of academic health department partnerships. In this session, participants will gain a foundational understanding of AHD partnerships and hear how the Public Health Institute's Center for Health and Leadership and Impact is supported by the California Endowment, developed by the California Academic Health Department Initiative. The CAHD initiative successfully bridged the gap between local health departments and academic programs of public health to create a structured workforce pathway system. Find more at the link in the show notes.

Join ASTHO and the Public Health Foundation on June 30 for another webinar focused on academic health department partnerships. In this session, participants will gain a foundational understanding of AHD partnerships and hear how North Dakota Health and Human Services, in partnership with North Dakota State University, created a student assistantship program. This initiative offers Master of Public Health students hands-on experience, builds the leadership skills of early career staff, and strengthens overall public health capacity. Register at the link in the show notes.

This has been Public Health Review Morning Edition. I'm John Sheehan for the Association of State and Territorial Health Officials.